The Challenge of Collaborative IoT-Based Inferencing in Adversarial Settings

In many practical environments, resourceconstrained IoT nodes are deployed with varying degrees of redundancy/overlap-i.e., their data streams possess significant spatiotemporal correlation. We posit that collaborative inferencing, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). However, such collaborative models are vulnerable to adversarial behavior by one or more nodes, and thus require mechanisms that identify and inoculate against such malicious behavior. We use a dataset of 8 outdoor cameras to (a) demonstrate that such collaborative inferencing can improve people counting accuracy by over 8%, and (b) show how a dynamic reputation mechanism preserves such gains even if some cameras behave maliciously.